MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078365 A) filed by Cmr Engineering College, Kandlakoyav, Medchal Road, Hyderabad, Medchal Malkajgiri, Telangana-, India. on June 25, 2026, for Predictive Maintenance Framework For Industrial Equipment Using Ensemble Machine Learning Models.
Inventors include Dr. Suman Mishra, Professor, Electronics And; Mrs. R. P. Shanthi Rani, Assistant Professor, Computer Science And Engineering Aiml, Cmr Engineering College, Kandlakoya, Hydeabad-; Mrs. Jhansi Lakshmi, Assistant Professor, Computer; Mr. Azhar Mohammed, Assistant Professor, Computer; Mrs. M. Soujanya, Assistant Professor, Computer Science; Mrs. Parnandhi Renuka, Assistant Professor, Computer Science And Engineering Data Science; and Mr. B. Kumaraswamy, Associate Professor, Computer Science And Engineering Data Science.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention discloses a Predictive Maintenance Framework for Industrial Equipment Using Ensemble Machine Learning Models for intelligent monitoring and maintenance of industrial assets. The framework is designed to predict equipment failures before their occurrence, thereby reducing unplanned downtime, maintenance costs, and operational risks. The system utilizes Industrial Internet of Things (IIoT) devices and sensors to continuously collect real-time operational data including temperature, vibration, pressure, current, and acoustic signals from industrial equipment. The collected data undergo preprocessing steps such as cleaning, normalization, feature extraction, and noise reduction to improve data quality and model performance. The framework employs ensemble machine learning techniques by integrating multiple predictive algorithms including Random Forest, XGBoost, Gradient Boosting, and Support Vector Machines to enhance prediction accuracy and robustness. The ensemble model analyzes equipment health, predicts failure probability, and estimates the remaining useful life of machinery. Based on prediction results, the system generates maintenance alerts and recommends appropriate maintenance actions for preventing unexpected failures. The framework further supports cloud and edge computing architectures for scalable deployment across manufacturing plants, smart factories, power generation units, and industrial automation systems. By enabling real-time equipment monitoring and intelligent maintenance scheduling, the invention improves operational efficiency, increases equipment lifespan, enhances safety, and contributes to cost-effective and reliable industrial operations. The proposed system provides an adaptive and scalable solution for next-generation predictive maintenance applications in Industry 4.0 environments.
Disclaimer: Curated by HT Syndication.
